基于蒲公英优化算法的光伏系统最大功率点跟踪

Maximum Power Point Tracking of Photovoltaic System Based on Dandelion Optimization Algorithm

针对局部阴影导致光伏系统功率输出特性曲线呈现多峰值,最大功率追踪(Maximum power point tracking,MPPT)算法存在容易陷入局部极值、输出功率振荡等问题,将收敛快速、精度高和稳定性好的蒲公英优化(Dandelion optimization,DO)算法应用于光伏系统MPPT中。DO算法采用非线性减小的随机扰动因子α、向下凸振荡k值在MPPT初期阶段充分搜索全局区域,后期转向开发局部区域,确保输出功率稳定和精确收敛于最优功率;再结合不规则布朗运动逃避局部功率极值点,采用Levy飞行函数增强局部功率的搜索能力,最终算法收敛于全局最大功率。仿真结果表明,与灰狼算法(Grey wolf algorithm,GWO)、粒子群算法(Particle swarm algorithm,PSO)相比,DO算法具有追踪效率高、稳定性好、鲁棒性强等优点。

As the power output characteristic curve of photovoltaic system shows multi-peak because of the local shadow,and the maximum power tracking algorithm is prone to fall into local extremum and output power oscillation,the dandelion optimization algorithm with fast convergence,high precision and good stability is applied to MPPT of photovoltaic system.The DO algorithm uses the nonlinearly reduced random disturbance factorαand the downward convex oscillation k value to fully search in the initial stage of MPPT,and then turns to develop the local region to ensure the output power is stable and accurately converging to the optimal power,and the combines the irregular Brounian motion to escape the local power extreme point.Finally,the Levy flight function is used to enhance the search ability of local power,and the algorithm converges to the global maximum power.The simulation results show that compared with the gray wolf algorithm and the particle swarm optimization algorithm,DO algorithm has the advantages of high tracking efficiency,good stability,and strong robustness.